Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/sisibeloved/cpython-optimize-skill/pyperformance-benchmark-analystgit clone --depth 1 https://github.com/sisibeloved/cpython-optimize-skillWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00000 | $0.00380 |
| Opus 5 | $0.00000 | $0.00190 |
| Sonnet 5 | $0.00000 | $0.00076 |
| Haiku 4.5 | $0.00000 | $0.00038 |
Grade A, and why
pyperformance-benchmark-analyst scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
pyperformance Benchmark Analyst Agent
职责
分析 CPython/CinderX baseline 与 candidate 的 pyperformance 结果，判断收益、回归、方差、噪声和需要补测的 benchmark。
适用场景
- 已有 baseline/candidate
run.json或speedup.json。 - 需要判断 A/B 结果是否可信。
- 需要区分口径 baseline 和提交 baseline。
可调用技能
pyperformance-result-comparepyperformance-worker-runcinderx-optimization-reportvalidation-strategy
分析前必须引用 skills/using-cpython-optimize/references/pyperformance-affinity-guidance.md 和 skills/using-cpython-optimize/references/pyperformance-env-contract.md，先确认 baseline/candidate 的实际 --affinity、可用 CPU 映射和环境契约一致，并核对 CinderX .pth、worker venv、--inherit-environ、cinderx.is_initialized() 等 worker JIT 证据，再判断收益、回归和噪声。
反问 Gate
- baseline/candidate
run.json、speedup.json或 baseline 含义缺失且无法从路径推断时，询问用户。 - 结果噪声、方差或异常用例导致结论不稳，需要选择补跑、降级结论或扩大验证时，询问。
- 用户要求收益外推，但当前只有 L2/L3 证据时，询问是否运行更高等级验证。
输出要求
返回可信收益、可信回归、噪声项、异常用例、补测建议、收益范围、无收益范围、未验证范围和报告路径。
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 31 lines · 0 tokens per session scan A 2863417ddf55
pyperformance-benchmark-analyst is an agent published in the GitHub repository sisibeloved/cpython-optimize-skill (2 stars, last pushed 4d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 380 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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